In image processing, discerning the structure of an image is often a challenging undertaking. The structure of an image may be characterized by many attributes, such as fractal, aliased, noisy, flat or featureless, periodic, and the like. Each of these characteristics poses a challenge to analyzing the structure.
Periodic patterns, for example, can assume almost any shape, at almost any scale, may range widely in frequency of occurrence, and may occur at any location in the image. Accordingly, detecting the presence of periodic patterns with these degrees of freedom may be difficult. In particular, it may be a more difficult problem than detecting a harmonic such as a pure tone at 20 kHz. Therefore, a simple Fourier transform will not easily reveal many of the essential properties of a periodic pattern. Additionally, content structure and content spectra should not be confused, despite the fact that the mappings may be one-to-one.
Since periodic patterns range from totally featureless, (i.e., flat content having zero period), all the way up to the Nyquist frequency and beyond where they can degenerate into essentially chaotic content, examining the fully normalized phase plane correlation surface for periodicity is often not useful. Additionally, a combination or super-positioning of two or more types of periodic patterns can appear simultaneously when they are spatially co-located. Accordingly, normalized phase plane correlation can mask characteristics in the content structure.
Many attempts have been made to determine the content of image structure. However, the vast majority of these attempts focus on using the spectrum of the Fourier transform for this purpose, which is not very effective. Accordingly, it would be beneficial to provide a method and apparatus for processing image data that aids in effectively determining the content of an image.